Clinical and histopathological responses to bee venom phonophoresis in treating venous and diabetic ulcers: a single-blind randomized controlled trial

IntroductionChronic venous and diabetic ulcers are hard to treat that cause patients long time of suffering as well as significant healthcare and financial costs.PurposeThe conducted study was to evaluate the efficacy of bee venom (BV) phonophoresis on the healing of chronic unhealed venous and/or diabetic foot ulcers Also, to compare the healing rate of diabetic and venous ulcers.MethodologyThe study included 100 patients (71 males and 29 females) with an age range of 40-60 years' old who had chronic unhealed venous leg ulcers of grade I, grade II, or diabetic foot ulcers with type II diabetes mellitus. They randomly assigned into four equal groups of 25: Group A (diabetic foot ulcer study group) and group C (venous ulcer study group) who both received conservative treatment of medical ulcer care and phonophoresis with BV gel, in addition to group B (diabetic foot ulcer control group) and group D (venous ulcer control group) who both received conservative treatment of medical ulcer care and received ultrasound sessions only without BV gel. Wound surface area (WSA) and ulcer volume measurement (UVM) were used to assess the ulcer healing pre-application (P0), post-6 weeks of treatment (P1), and after 12 weeks of treatment (P2). In addition to Ki-67 immunohistochemistry was used to evaluate the cell proliferative in the granulation tissue of ulcers pre-application (P0) and after 12 weeks of treatment (P2) for all groups.ResultsThis research revealed a statistical significance improvement (p ≤ 0.0) in the WSA, and UVM with no significant difference between study groups after treatment. Regarding Ki-67 immunohistochemistry showed higher post treatment values in the venous ulcer group in comparison to the diabetic foot ulcer group.ConclusionBee venom (BV) provided by phonophoresis is effective adjuvant treatment in accelerating venous and diabetic foot ulcer healing with higher proliferative effect on venous ulcer.Clinical trial registrationwww.ClinicalTrials.gov, identifier: NCT05285930

Mid-infrared supercontinuum generation using dispersion-engineered Ge11.5As24Se64.5 chalcogenide channel waveguide

We numerically investigate mid-infrared supercontinuum (SC) generation in dispersion-engineered, air-clad, Ge11.5As24Se64.5 chalcogenide-glass channel waveguides employing two different materials, Ge11.5As24S64.5 or MgF2 glass for their lower cladding. We study the effect of waveguide parameters on the bandwidth of the SC at the output of 1-cm-long waveguide. Our results show that output can vary over a wide range depending on its design and the pump wavelength employed. At the pump wavelength of 2 µm the SC never extended beyond 4.5 µm for any of our designs. However, supercontinuum could be extended to beyond 5 µm for a pump wavelength of 3.1 µm. A broadband SC spanning from 2 µm to 6 µm and extending over 1.5 octave could be generated with a moderate peak power of 500 W at a pump wavelength of 3.1 µm using an air-clad, all-chalcogenide, channel waveguide. We show that SC can be extended even further when MgF2 glass is used for the lower cladding of chalcogenide waveguide. Our numerical simulations produced SC spectra covering the wavelength range 1.8-7.7 µm (> two octaves) by using this geometry. Both ranges exceed the broadest SC bandwidths reported so far. Moreover, we realize it using 3.1 µm pump source and relatively low peak power pulses. By employing the same pump source, we show that SC spectra can cover a wavelength range of 1.8-11 µm (> 2.5 octaves) in a channel waveguide employing MgF2 glass for its lower cladding with a moderate peak power of 3000 W

Design and modeling of dispersion-engineered all-chalcogenide triangular-core fiber for mid-infrared-region supercontinuum generation

An ultrabroadband mid-infrared supercontinuum (SC) source has been designed and modeled using a 10-mm-long all-chalcogenide triangular-core fiber (TCF). The TCF structure can be fabricated from Ge11.5As24Se64.5 chalcogenide glass as a core and Ge11.5As24S64.5 chalcogenide glass for its cladding running along the length of the fiber instead of air holes. Assuming the pump operates at 4 μm, the TCF is optimized by varying its side length using both anomalous-dispersion and all-normal-dispersion SC generation. Mid-infrared-region SC spectral broadening spanning beyond 15 μm could be generated with a low peak power of 3 kW by the proposed TCF structure optimized with varying its side length between 7 and 8 μm in anomalous-dispersion pumping. On the other hand, the TCF side length has to be decreased to 5.5 μm and below to optimize it for pumping in all-normal-dispersion-region SC generation. A coherent flat-top SC evolution in the mid-infrared region of up to 7 μm could be observed by this design with the same pump peak power and pulse duration applied before. The ultrawide optical bandwidth obtained by the proposed TCF design can be an effective tool for mid-infrared-region applications such as optical coherence tomography, molecular fingerprint spectroscopy, and biomedical imaging

Generation of an ultrabroadband supercontinuum in the mid-infrared region using dispersion-engineered GeAsSe photonic crystal fiber

An ultrabroadband mid-infrared (MIR) region supercontinuum (SC) is demonstrated numerically through dispersion-engineered traditional chalcogenide (ChG) photonic crystal fiber (PCF). By varying structural parameters pitch (hole to hole spacing) and air-hole diameter to pitch ratio, a number of 10-mm-long hexagonal PCFs made employing GeAsSe ChG glass as a core and air-holes of hexagonal lattice running through their lengths as a cladding are optimized to predict an efficient mid-infrared region SC spectral emission by pumping them using a tunable pump source between 2.9 and 3.3 µm. Simulations are carried out using an ultrashort pump pulse of 100-fs duration with a low pulse peak powers of between 3 and 4 kW into the optimized designs. It is found through numerical analysis that efficient SC spectral broadening with flattened output can be obtained by increasing the PCF pitch rather than increasing the PCF cladding containing air-hole diameter although a larger nonlinear coefficient could be obtained through increasing air-hole diameter of an optimized design. Simulation results show that the SC spectra can be broadened up to 12.2 µm for a certain design with a peak power of 3 kW. Using a peak power of 4 kW, it is possible to obtain SC spectral broadening beyond 14 µm with an optimized design spanning the wavelength range from 1.8 to 14 µm which covers the electromagnetic spectrum required for MIR molecular fingerprint region applications such as sensing and biological imaging

Split and Merge LEACH Based Routing Algorithm for Wireless Sensor Networks

Hierarchical routing and clustering mechanisms in Wireless Sensor Networks (WSNs) help to reduce both the energy consumption, and the overhead that is created when all the sensor nodes in the network are sending information to the central data collection point or base station. LEACH (Low Energy Adaptive Clustering Hierarchy) is one of the most well-known energy efficient clustering algorithms for WSNs. In this paper, we extend the LEACH protocol to (LEACH-SM) protocol by introducing a Split and Merge stage to improve the performance and robustness. We consider the following design aspects: non-uniform distribution of sensors, cluster re-formation by splitting or merging clusters conditionally, routes maintenance, and nodes mobility. OPNET, a well-known simulator tool, is used to simulate LEACH-SM in order to evaluate the performance of the proposed protocol. Simulation results and comparisons with existing protocols show the effectiveness and strength of the proposed protocol in terms of enhancing the lifetime of the whole sensor network, where sensors are either static or mobile with low speed

Regression Models to Estimate Accumulation Capability of Six Metals by Two Macrophytes, Typha domingensis and Typha elephantina, Grown in an Arid Climate in the Mountainous Region of Taif, Saudi Arabia

In this study, we explored the capacity for two promising macrophytes, Typha domingensis and Typha elephantina, to be used for the surveillance of contamination by six metals, i.e., Cu, Fe, Mn, Ni, Pb, and Zn, in the mountainous area of Taif City in Saudi Arabia. Regression models were generated in order to forecast the metal concentrations within the plants’ organs, i.e., the leaves, flowers, peduncles, rhizomes, and roots. The sediment mean values for pH and the six metals varied amongst the sampling locations for the respective macrophytes, indicating that similar life forms fail to indicate equivalent concentrations. For instance, dissimilar concentrations of the metals under investigation were observed within the organs of the two rooted macrophytes. The research demonstrated that the segregation of metals is a regular event in all the investigated species in which the metal concentrations vary amongst the different plant constituent types. In the current study, T. domingensis and T. elephantina varied in their capacity to absorb specific metals; the bioaccumulation of metals was greater within T. domingensis. The relationships between the observed and model-estimated metal levels, in combination with high R2 and modest mean averaged errors, offered an appraisal of the goodness of fit of most of the generated models. The t-tests revealed no variations between the observed and model-estimated concentrations of the six metals under investigation within the organs of the two macrophytes, which emphasised the precision of the models. These models offer the ability to perform hazard appraisals within ecosystems and to determine the reference criteria for sediment metal concentration. Lastly, T. domingensis and T. elephantina exhibit the potential for bioaccumulation for the alleviation of contamination from metals

CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

YesThe quantification of corneal endothelial cell (CEC) morphology using manual and semi-automatic software enables an objective assessment of corneal endothelial pathology. However, the procedure is tedious, subjective, and not widely applied in clinical practice. We have developed the CellsDeepNet system to automatically segment and analyse the CEC morphology. The CellsDeepNet system uses Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of the CEC images and reduce the effects of non-uniform image illumination, 2D Double-Density Dual-Tree Complex Wavelet Transform (2DDD-TCWT) to reduce noise, Butterworth Bandpass filter to enhance the CEC edges, and moving average filter to adjust for brightness level. An improved version of U-Net was used to detect the boundaries of the CECs, regardless of the CEC size. CEC morphology was measured as mean cell density (MCD, cell/mm2), mean cell area (MCA, µm2), mean cell perimeter (MCP, µm), polymegathism (coefficient of CEC size variation), and pleomorphism (percentage of hexagonality coefficient). The CellsDeepNet system correlated highly significantly with the manual estimations for MCD (r = 0.94), MCA (r = 0.99), MCP (r = 0.99), polymegathism (r = 0.92), and pleomorphism (r = 0.86), with

CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

The quantification of corneal endothelial cell (CEC) morphology using manual and semi-automatic software enables an objective assessment of corneal endothelial pathology. However, the procedure is tedious, subjective, and not widely applied in clinical practice. We have developed the CellsDeepNet system to automatically segment and analyse the CEC morphology. The CellsDeepNet system uses Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of the CEC images and reduce the effects of non-uniform image illumination, 2D Double-Density Dual-Tree Complex Wavelet Transform (2DDD-TCWT) to reduce noise, Butterworth Bandpass filter to enhance the CEC edges, and moving average filter to adjust for brightness level. An improved version of U-Net was used to detect the boundaries of the CECs, regardless of the CEC size. CEC morphology was measured as mean cell density (MCD, cell/mm2), mean cell area (MCA, μm2), mean cell perimeter (MCP, μm), polymegathism (coefficient of CEC size variation), and pleomorphism (percentage of hexagonality coefficient). The CellsDeepNet system correlated highly significantly with the manual estimations for MCD (r = 0.94), MCA (r = 0.99), MCP (r = 0.99), polymegathism (r = 0.92), and pleomorphism (r = 0.86), with p < 0.0001 for all the extracted clinical features. The Bland–Altman plots showed excellent agreement. The percentage difference between the manual and automated estimations was superior for the CellsDeepNet system compared to the CEAS system and other state-of-the-art CEC segmentation systems on three large and challenging corneal endothelium image datasets captured using two different ophthalmic devices

Adaptive deep learning detection model for multi-foggy images

The fog has different features and effects within every single environment. Detection whether there is fog in the image is considered a challenge and giving the type of fog has a substantial enlightening effect on image defogging. Foggy scenes have different types such as scenes based on fog density level and scenes based on fog type. Machine learning techniques have a significant contribution to the detection of foggy scenes. However, most of the existing detection models are based on traditional machine learning models, and only a few studies have adopted deep learning models. Furthermore, most of the existing machines learning detection models are based on fog density-level scenes. However, to the best of our knowledge, there is no such detection model based on multi-fog type scenes have presented yet. Therefore, the main goal of our study is to propose an adaptive deep learning model for the detection of multi-fog types of images. Moreover, due to the lack of a publicly available dataset for inhomogeneous, homogenous, dark, and sky foggy scenes, a dataset for multi-fog scenes is presented in this study (https://github.com/Karrar-H-Abdulkareem/Multi-Fog-Dataset). Experiments were conducted in three stages. First, the data collection phase is based on eight resources to obtain the multi-fog scene dataset. Second, a classification experiment is conducted based on the ResNet-50 deep learning model to obtain detection results. Third, evaluation phase where the performance of the ResNet-50 detection model has been compared against three different models. Experimental results show that the proposed model has presented a stable classification performance for different foggy images with a 96% score for each of Classification Accuracy Rate (CAR), Recall, Precision, F1-Score which has specific theoretical and practical significance. Our proposed model is suitable as a pre-processing step and might be considered in different real-time applications.Web of Science77372